Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f2fb96a3f60>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f2fb95d73c8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels])
    z_input = tf.placeholder(tf.float32, [None, z_dim])
    learning_rate = tf.placeholder(tf.float32)

    return real_input, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/usr/lib/python3.5/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/usr/local/lib/python3.5/dist-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/usr/local/lib/python3.5/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/usr/local/lib/python3.5/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-1f24332fee50>", line 23, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/root/sharedfolder/16.face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/root/sharedfolder/16.face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/root/sharedfolder/16.face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/root/sharedfolder/16.face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_should_use.py", line 170, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_should_use.py", line 139, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_should_use.py", line 96, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False, alpha=0.2, is_train=True):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    with tf.variable_scope('discriminator', reuse=reuse):
        
        # Inputs 28x28x3
        
        x1 = tf.layers.conv2d(images, filters=64, kernel_size=5, strides=2, padding='same')
        # no batchnorm for first conv layer
        x1 = tf.maximum(alpha*x1, x1)
        # Layer output 14x14x64
        
        x2 = tf.layers.conv2d(x1, filters=128, kernel_size=5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha*x2, x2)
        # Layer output 7x7x128
        
        x3 = tf.layers.conv2d(x2, filters=256, kernel_size=5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha*x3, x3)
        # Layer output 4x4x128
        
        # Flattening
        f = tf.reshape(x3, (-1, 4*4*256))
        logits = tf.layers.dense(f, 1) # one output neuron for binary classification
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    # took me days to figure out that function needs to maintain 3 positional params
    alpha = 0.2
    
    with tf.variable_scope('generator', reuse=not is_train):
        # Fully connected layer and reshaping
        x1 = tf.layers.dense(z, 7*7*256)
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha*x1, x1)
        # Layer output 7x7x256
        
        # Conv layer
        x2 = tf.layers.conv2d_transpose(x1, filters=128, kernel_size=5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha*x2, x2)
        # Layer output 14x14x128
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x2, filters=out_channel_dim, kernel_size=5, strides=2, padding='same')
        # Output 28x28x3
        
        out = tf.tanh(logits)
    
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    smooth = 0.1
    
    g_model = generator(input_z, out_channel_dim)
    
    d_real_model, d_real_logits = discriminator(input_real)
    d_fake_model, d_fake_logits = discriminator(g_model, reuse=True)
    
    d_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_real_logits, labels=tf.ones_like(d_real_model) * (1-smooth)))
    
    d_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_fake_logits, labels=tf.zeros_like(d_fake_model)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_fake_logits, labels=tf.ones_like(d_fake_model)))
    
    d_loss = d_real_loss + d_fake_loss
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    
    d_vars = [v for v in t_vars if v.name.startswith('discriminator')]
    g_vars = [v for v in t_vars if v.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_opt, g_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model

    image_channels = 3 if data_image_mode=='RGB' else 1
    real_input, z_input, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    
    d_loss, g_loss = model_loss(real_input, z_input, image_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    steps = -1
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                steps += 1
                # creating noise input for generator 
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # normalizing image inputs
                batch_images *= 2
                
                # running D optimizer
                _ = sess.run(d_opt, feed_dict={real_input: batch_images, z_input: batch_z, lr: learning_rate})
                
                # running G optimizer twice for better performance
                _ = sess.run(g_opt, feed_dict={z_input: batch_z, real_input: batch_images, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={z_input: batch_z, real_input: batch_images, lr: learning_rate})
                
                if steps % 10 == 0:
                    # displaying training losses
                    t_loss_d = d_loss.eval({z_input: batch_z, real_input: batch_images})
                    t_loss_g = g_loss.eval({z_input: batch_z})
                    print('**********************************')
                    print('Step: {}'.format(steps+1))
                    print('Epoch: {}/{}'.format(epoch_i+1, epoch_count))
                    print('D Loss: {:.4f}'.format(t_loss_d))
                    print('G Loss: {:.4f}'.format(t_loss_g))
                    
                    # displaying a sample of generated images
                    if steps % 100 == 0: 
                        print('**********************************')
                        show_generator_output(sess, batch_size, z_input, image_channels, data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [18]:
batch_size = 64
z_dim = 500
learning_rate = 0.0005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
**********************************
Step: 1
Epoch: 1/2
D Loss: 7.2613
G Loss: 0.0027
**********************************
**********************************
Step: 11
Epoch: 1/2
D Loss: 1.1687
G Loss: 3.3744
**********************************
Step: 21
Epoch: 1/2
D Loss: 3.2479
G Loss: 0.0852
**********************************
Step: 31
Epoch: 1/2
D Loss: 2.2428
G Loss: 0.4325
**********************************
Step: 41
Epoch: 1/2
D Loss: 2.5220
G Loss: 0.1423
**********************************
Step: 51
Epoch: 1/2
D Loss: 2.3074
G Loss: 0.2165
**********************************
Step: 61
Epoch: 1/2
D Loss: 2.3103
G Loss: 0.1878
**********************************
Step: 71
Epoch: 1/2
D Loss: 2.1523
G Loss: 0.2085
**********************************
Step: 81
Epoch: 1/2
D Loss: 2.2203
G Loss: 0.2057
**********************************
Step: 91
Epoch: 1/2
D Loss: 1.7942
G Loss: 0.5145
**********************************
Step: 101
Epoch: 1/2
D Loss: 1.9291
G Loss: 0.2687
**********************************
**********************************
Step: 111
Epoch: 1/2
D Loss: 2.0976
G Loss: 0.2076
**********************************
Step: 121
Epoch: 1/2
D Loss: 1.8247
G Loss: 0.6855
**********************************
Step: 131
Epoch: 1/2
D Loss: 1.6941
G Loss: 0.3744
**********************************
Step: 141
Epoch: 1/2
D Loss: 1.5570
G Loss: 0.5375
**********************************
Step: 151
Epoch: 1/2
D Loss: 1.6330
G Loss: 0.5495
**********************************
Step: 161
Epoch: 1/2
D Loss: 2.4758
G Loss: 0.1377
**********************************
Step: 171
Epoch: 1/2
D Loss: 1.9500
G Loss: 0.2619
**********************************
Step: 181
Epoch: 1/2
D Loss: 1.5649
G Loss: 0.6659
**********************************
Step: 191
Epoch: 1/2
D Loss: 1.8442
G Loss: 0.2915
**********************************
Step: 201
Epoch: 1/2
D Loss: 1.4991
G Loss: 0.6351
**********************************
**********************************
Step: 211
Epoch: 1/2
D Loss: 2.7148
G Loss: 0.1120
**********************************
Step: 221
Epoch: 1/2
D Loss: 1.5588
G Loss: 0.6873
**********************************
Step: 231
Epoch: 1/2
D Loss: 1.7657
G Loss: 0.3698
**********************************
Step: 241
Epoch: 1/2
D Loss: 2.0611
G Loss: 0.2235
**********************************
Step: 251
Epoch: 1/2
D Loss: 1.4925
G Loss: 0.8323
**********************************
Step: 261
Epoch: 1/2
D Loss: 1.5510
G Loss: 0.4763
**********************************
Step: 271
Epoch: 1/2
D Loss: 1.7880
G Loss: 0.4378
**********************************
Step: 281
Epoch: 1/2
D Loss: 1.5601
G Loss: 0.8036
**********************************
Step: 291
Epoch: 1/2
D Loss: 2.6435
G Loss: 0.1193
**********************************
Step: 301
Epoch: 1/2
D Loss: 1.8587
G Loss: 0.3303
**********************************
**********************************
Step: 311
Epoch: 1/2
D Loss: 2.4611
G Loss: 0.1496
**********************************
Step: 321
Epoch: 1/2
D Loss: 2.3809
G Loss: 0.1591
**********************************
Step: 331
Epoch: 1/2
D Loss: 1.9108
G Loss: 0.2692
**********************************
Step: 341
Epoch: 1/2
D Loss: 1.8807
G Loss: 0.3001
**********************************
Step: 351
Epoch: 1/2
D Loss: 1.7019
G Loss: 1.5163
**********************************
Step: 361
Epoch: 1/2
D Loss: 2.0947
G Loss: 0.2225
**********************************
Step: 371
Epoch: 1/2
D Loss: 2.3067
G Loss: 0.1816
**********************************
Step: 381
Epoch: 1/2
D Loss: 1.4599
G Loss: 0.8966
**********************************
Step: 391
Epoch: 1/2
D Loss: 1.5151
G Loss: 0.9077
**********************************
Step: 401
Epoch: 1/2
D Loss: 2.1276
G Loss: 0.2277
**********************************
**********************************
Step: 411
Epoch: 1/2
D Loss: 1.9819
G Loss: 0.2581
**********************************
Step: 421
Epoch: 1/2
D Loss: 2.3985
G Loss: 0.1685
**********************************
Step: 431
Epoch: 1/2
D Loss: 1.6278
G Loss: 0.5022
**********************************
Step: 441
Epoch: 1/2
D Loss: 2.4289
G Loss: 0.1535
**********************************
Step: 451
Epoch: 1/2
D Loss: 1.8375
G Loss: 0.3015
**********************************
Step: 461
Epoch: 1/2
D Loss: 1.5041
G Loss: 0.7632
**********************************
Step: 471
Epoch: 1/2
D Loss: 1.7223
G Loss: 0.3591
**********************************
Step: 481
Epoch: 1/2
D Loss: 1.6823
G Loss: 0.4452
**********************************
Step: 491
Epoch: 1/2
D Loss: 2.3257
G Loss: 2.0026
**********************************
Step: 501
Epoch: 1/2
D Loss: 1.4040
G Loss: 0.6196
**********************************
**********************************
Step: 511
Epoch: 1/2
D Loss: 1.4346
G Loss: 0.6350
**********************************
Step: 521
Epoch: 1/2
D Loss: 1.5504
G Loss: 0.5930
**********************************
Step: 531
Epoch: 1/2
D Loss: 1.7830
G Loss: 0.3364
**********************************
Step: 541
Epoch: 1/2
D Loss: 1.5426
G Loss: 0.6367
**********************************
Step: 551
Epoch: 1/2
D Loss: 1.7910
G Loss: 0.5420
**********************************
Step: 561
Epoch: 1/2
D Loss: 1.6686
G Loss: 0.4157
**********************************
Step: 571
Epoch: 1/2
D Loss: 1.9565
G Loss: 0.2659
**********************************
Step: 581
Epoch: 1/2
D Loss: 1.8858
G Loss: 0.3080
**********************************
Step: 591
Epoch: 1/2
D Loss: 1.8912
G Loss: 0.2784
**********************************
Step: 601
Epoch: 1/2
D Loss: 3.7785
G Loss: 0.0491
**********************************
**********************************
Step: 611
Epoch: 1/2
D Loss: 1.5239
G Loss: 0.4674
**********************************
Step: 621
Epoch: 1/2
D Loss: 1.6730
G Loss: 0.3860
**********************************
Step: 631
Epoch: 1/2
D Loss: 1.3004
G Loss: 0.6858
**********************************
Step: 641
Epoch: 1/2
D Loss: 1.7012
G Loss: 0.4125
**********************************
Step: 651
Epoch: 1/2
D Loss: 1.6055
G Loss: 0.4326
**********************************
Step: 661
Epoch: 1/2
D Loss: 1.3755
G Loss: 0.5136
**********************************
Step: 671
Epoch: 1/2
D Loss: 1.6057
G Loss: 0.4220
**********************************
Step: 681
Epoch: 1/2
D Loss: 1.7954
G Loss: 0.3208
**********************************
Step: 691
Epoch: 1/2
D Loss: 1.7658
G Loss: 1.9295
**********************************
Step: 701
Epoch: 1/2
D Loss: 1.5384
G Loss: 0.4618
**********************************
**********************************
Step: 711
Epoch: 1/2
D Loss: 1.5416
G Loss: 0.6000
**********************************
Step: 721
Epoch: 1/2
D Loss: 2.5206
G Loss: 0.1559
**********************************
Step: 731
Epoch: 1/2
D Loss: 1.8566
G Loss: 0.3053
**********************************
Step: 741
Epoch: 1/2
D Loss: 1.5742
G Loss: 0.3946
**********************************
Step: 751
Epoch: 1/2
D Loss: 1.6310
G Loss: 0.3802
**********************************
Step: 761
Epoch: 1/2
D Loss: 1.4141
G Loss: 0.5159
**********************************
Step: 771
Epoch: 1/2
D Loss: 1.4193
G Loss: 0.5011
**********************************
Step: 781
Epoch: 1/2
D Loss: 1.3531
G Loss: 0.5363
**********************************
Step: 791
Epoch: 1/2
D Loss: 1.7068
G Loss: 0.3399
**********************************
Step: 801
Epoch: 1/2
D Loss: 2.0157
G Loss: 0.3126
**********************************
**********************************
Step: 811
Epoch: 1/2
D Loss: 2.1827
G Loss: 0.2052
**********************************
Step: 821
Epoch: 1/2
D Loss: 1.8166
G Loss: 0.3096
**********************************
Step: 831
Epoch: 1/2
D Loss: 1.3319
G Loss: 0.5761
**********************************
Step: 841
Epoch: 1/2
D Loss: 1.4137
G Loss: 0.5291
**********************************
Step: 851
Epoch: 1/2
D Loss: 1.3456
G Loss: 0.5566
**********************************
Step: 861
Epoch: 1/2
D Loss: 1.5033
G Loss: 0.4407
**********************************
Step: 871
Epoch: 1/2
D Loss: 1.6255
G Loss: 0.3752
**********************************
Step: 881
Epoch: 1/2
D Loss: 1.6629
G Loss: 1.2026
**********************************
Step: 891
Epoch: 1/2
D Loss: 1.6195
G Loss: 0.3939
**********************************
Step: 901
Epoch: 1/2
D Loss: 1.4770
G Loss: 0.5245
**********************************
**********************************
Step: 911
Epoch: 1/2
D Loss: 1.6847
G Loss: 0.3503
**********************************
Step: 921
Epoch: 1/2
D Loss: 1.7344
G Loss: 0.3684
**********************************
Step: 931
Epoch: 1/2
D Loss: 1.4546
G Loss: 0.4355
**********************************
Step: 941
Epoch: 2/2
D Loss: 2.2593
G Loss: 0.1886
**********************************
Step: 951
Epoch: 2/2
D Loss: 1.2993
G Loss: 0.7690
**********************************
Step: 961
Epoch: 2/2
D Loss: 1.4515
G Loss: 0.4721
**********************************
Step: 971
Epoch: 2/2
D Loss: 1.2794
G Loss: 0.5736
**********************************
Step: 981
Epoch: 2/2
D Loss: 2.1470
G Loss: 0.2184
**********************************
Step: 991
Epoch: 2/2
D Loss: 1.2518
G Loss: 0.6269
**********************************
Step: 1001
Epoch: 2/2
D Loss: 1.5086
G Loss: 0.4599
**********************************
**********************************
Step: 1011
Epoch: 2/2
D Loss: 1.6212
G Loss: 0.4471
**********************************
Step: 1021
Epoch: 2/2
D Loss: 2.6833
G Loss: 0.1249
**********************************
Step: 1031
Epoch: 2/2
D Loss: 1.5955
G Loss: 0.4103
**********************************
Step: 1041
Epoch: 2/2
D Loss: 1.6759
G Loss: 0.3774
**********************************
Step: 1051
Epoch: 2/2
D Loss: 1.6858
G Loss: 0.3448
**********************************
Step: 1061
Epoch: 2/2
D Loss: 1.5134
G Loss: 0.4386
**********************************
Step: 1071
Epoch: 2/2
D Loss: 1.1227
G Loss: 0.7290
**********************************
Step: 1081
Epoch: 2/2
D Loss: 2.2286
G Loss: 0.2064
**********************************
Step: 1091
Epoch: 2/2
D Loss: 1.4474
G Loss: 0.8108
**********************************
Step: 1101
Epoch: 2/2
D Loss: 1.2820
G Loss: 0.6230
**********************************
**********************************
Step: 1111
Epoch: 2/2
D Loss: 1.2902
G Loss: 0.5788
**********************************
Step: 1121
Epoch: 2/2
D Loss: 1.9992
G Loss: 0.2670
**********************************
Step: 1131
Epoch: 2/2
D Loss: 1.2419
G Loss: 0.6785
**********************************
Step: 1141
Epoch: 2/2
D Loss: 1.3241
G Loss: 0.5737
**********************************
Step: 1151
Epoch: 2/2
D Loss: 1.5370
G Loss: 0.4270
**********************************
Step: 1161
Epoch: 2/2
D Loss: 2.4485
G Loss: 0.1562
**********************************
Step: 1171
Epoch: 2/2
D Loss: 1.9900
G Loss: 0.2696
**********************************
Step: 1181
Epoch: 2/2
D Loss: 1.9572
G Loss: 0.3022
**********************************
Step: 1191
Epoch: 2/2
D Loss: 1.8390
G Loss: 0.3302
**********************************
Step: 1201
Epoch: 2/2
D Loss: 1.4316
G Loss: 0.4830
**********************************
**********************************
Step: 1211
Epoch: 2/2
D Loss: 1.9243
G Loss: 0.3064
**********************************
Step: 1221
Epoch: 2/2
D Loss: 1.3480
G Loss: 0.7428
**********************************
Step: 1231
Epoch: 2/2
D Loss: 1.4860
G Loss: 0.6346
**********************************
Step: 1241
Epoch: 2/2
D Loss: 1.8430
G Loss: 0.3493
**********************************
Step: 1251
Epoch: 2/2
D Loss: 1.8005
G Loss: 0.3286
**********************************
Step: 1261
Epoch: 2/2
D Loss: 1.8373
G Loss: 0.2933
**********************************
Step: 1271
Epoch: 2/2
D Loss: 1.8698
G Loss: 0.3089
**********************************
Step: 1281
Epoch: 2/2
D Loss: 1.2901
G Loss: 0.7867
**********************************
Step: 1291
Epoch: 2/2
D Loss: 2.2565
G Loss: 0.1905
**********************************
Step: 1301
Epoch: 2/2
D Loss: 1.7109
G Loss: 0.3521
**********************************
**********************************
Step: 1311
Epoch: 2/2
D Loss: 1.7195
G Loss: 0.3528
**********************************
Step: 1321
Epoch: 2/2
D Loss: 1.6895
G Loss: 0.3632
**********************************
Step: 1331
Epoch: 2/2
D Loss: 1.6504
G Loss: 0.4002
**********************************
Step: 1341
Epoch: 2/2
D Loss: 2.2977
G Loss: 2.6701
**********************************
Step: 1351
Epoch: 2/2
D Loss: 1.9515
G Loss: 0.2709
**********************************
Step: 1361
Epoch: 2/2
D Loss: 1.5370
G Loss: 0.4719
**********************************
Step: 1371
Epoch: 2/2
D Loss: 1.7847
G Loss: 0.3250
**********************************
Step: 1381
Epoch: 2/2
D Loss: 2.2786
G Loss: 0.2062
**********************************
Step: 1391
Epoch: 2/2
D Loss: 0.7968
G Loss: 1.2119
**********************************
Step: 1401
Epoch: 2/2
D Loss: 1.3161
G Loss: 0.5647
**********************************
**********************************
Step: 1411
Epoch: 2/2
D Loss: 1.2831
G Loss: 0.6321
**********************************
Step: 1421
Epoch: 2/2
D Loss: 1.8315
G Loss: 0.3217
**********************************
Step: 1431
Epoch: 2/2
D Loss: 3.7618
G Loss: 0.0596
**********************************
Step: 1441
Epoch: 2/2
D Loss: 2.7360
G Loss: 2.8894
**********************************
Step: 1451
Epoch: 2/2
D Loss: 1.1007
G Loss: 0.8913
**********************************
Step: 1461
Epoch: 2/2
D Loss: 0.9985
G Loss: 1.0308
**********************************
Step: 1471
Epoch: 2/2
D Loss: 1.2592
G Loss: 0.5906
**********************************
Step: 1481
Epoch: 2/2
D Loss: 1.8060
G Loss: 0.3360
**********************************
Step: 1491
Epoch: 2/2
D Loss: 1.4749
G Loss: 0.4793
**********************************
Step: 1501
Epoch: 2/2
D Loss: 1.3372
G Loss: 0.5823
**********************************
**********************************
Step: 1511
Epoch: 2/2
D Loss: 2.3888
G Loss: 0.1758
**********************************
Step: 1521
Epoch: 2/2
D Loss: 1.4719
G Loss: 0.4753
**********************************
Step: 1531
Epoch: 2/2
D Loss: 1.2262
G Loss: 0.5874
**********************************
Step: 1541
Epoch: 2/2
D Loss: 1.5762
G Loss: 0.4374
**********************************
Step: 1551
Epoch: 2/2
D Loss: 1.4153
G Loss: 0.5136
**********************************
Step: 1561
Epoch: 2/2
D Loss: 1.8826
G Loss: 0.2949
**********************************
Step: 1571
Epoch: 2/2
D Loss: 1.8169
G Loss: 0.5372
**********************************
Step: 1581
Epoch: 2/2
D Loss: 1.3867
G Loss: 0.5375
**********************************
Step: 1591
Epoch: 2/2
D Loss: 1.6749
G Loss: 0.3630
**********************************
Step: 1601
Epoch: 2/2
D Loss: 1.1232
G Loss: 1.1482
**********************************
**********************************
Step: 1611
Epoch: 2/2
D Loss: 1.0883
G Loss: 0.8495
**********************************
Step: 1621
Epoch: 2/2
D Loss: 2.1489
G Loss: 0.2270
**********************************
Step: 1631
Epoch: 2/2
D Loss: 1.5582
G Loss: 0.4805
**********************************
Step: 1641
Epoch: 2/2
D Loss: 1.7491
G Loss: 0.3295
**********************************
Step: 1651
Epoch: 2/2
D Loss: 1.7636
G Loss: 0.3459
**********************************
Step: 1661
Epoch: 2/2
D Loss: 2.4489
G Loss: 0.1926
**********************************
Step: 1671
Epoch: 2/2
D Loss: 1.2612
G Loss: 0.6272
**********************************
Step: 1681
Epoch: 2/2
D Loss: 1.3542
G Loss: 0.5372
**********************************
Step: 1691
Epoch: 2/2
D Loss: 1.2251
G Loss: 0.8510
**********************************
Step: 1701
Epoch: 2/2
D Loss: 1.8357
G Loss: 0.3469
**********************************
**********************************
Step: 1711
Epoch: 2/2
D Loss: 1.2984
G Loss: 0.7095
**********************************
Step: 1721
Epoch: 2/2
D Loss: 1.3604
G Loss: 0.5256
**********************************
Step: 1731
Epoch: 2/2
D Loss: 1.2779
G Loss: 0.6755
**********************************
Step: 1741
Epoch: 2/2
D Loss: 1.6774
G Loss: 0.3615
**********************************
Step: 1751
Epoch: 2/2
D Loss: 1.2951
G Loss: 0.5997
**********************************
Step: 1761
Epoch: 2/2
D Loss: 1.2015
G Loss: 0.6769
**********************************
Step: 1771
Epoch: 2/2
D Loss: 2.7525
G Loss: 0.1452
**********************************
Step: 1781
Epoch: 2/2
D Loss: 2.0320
G Loss: 0.2829
**********************************
Step: 1791
Epoch: 2/2
D Loss: 1.6044
G Loss: 0.4497
**********************************
Step: 1801
Epoch: 2/2
D Loss: 2.9604
G Loss: 0.1204
**********************************
**********************************
Step: 1811
Epoch: 2/2
D Loss: 1.2048
G Loss: 0.9115
**********************************
Step: 1821
Epoch: 2/2
D Loss: 1.3800
G Loss: 0.7273
**********************************
Step: 1831
Epoch: 2/2
D Loss: 2.1549
G Loss: 0.2686
**********************************
Step: 1841
Epoch: 2/2
D Loss: 1.8840
G Loss: 0.3417
**********************************
Step: 1851
Epoch: 2/2
D Loss: 1.5525
G Loss: 0.4950
**********************************
Step: 1861
Epoch: 2/2
D Loss: 1.8673
G Loss: 0.2900
**********************************
Step: 1871
Epoch: 2/2
D Loss: 1.3914
G Loss: 0.5365

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [17]:
batch_size = 32
z_dim = 2000
learning_rate = 0.0005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
**********************************
Step: 1
Epoch: 1/1
D Loss: 9.2127
G Loss: 0.0002
**********************************
**********************************
Step: 11
Epoch: 1/1
D Loss: 6.0439
G Loss: 0.0051
**********************************
Step: 21
Epoch: 1/1
D Loss: 2.7967
G Loss: 0.2168
**********************************
Step: 31
Epoch: 1/1
D Loss: 4.0167
G Loss: 0.0608
**********************************
Step: 41
Epoch: 1/1
D Loss: 6.0396
G Loss: 0.0095
**********************************
Step: 51
Epoch: 1/1
D Loss: 3.6289
G Loss: 0.0891
**********************************
Step: 61
Epoch: 1/1
D Loss: 5.0903
G Loss: 0.0171
**********************************
Step: 71
Epoch: 1/1
D Loss: 3.8446
G Loss: 0.0453
**********************************
Step: 81
Epoch: 1/1
D Loss: 3.5116
G Loss: 0.0761
**********************************
Step: 91
Epoch: 1/1
D Loss: 3.5409
G Loss: 0.0853
**********************************
Step: 101
Epoch: 1/1
D Loss: 3.1821
G Loss: 0.1208
**********************************
**********************************
Step: 111
Epoch: 1/1
D Loss: 2.9714
G Loss: 0.1284
**********************************
Step: 121
Epoch: 1/1
D Loss: 2.6724
G Loss: 0.1487
**********************************
Step: 131
Epoch: 1/1
D Loss: 2.7839
G Loss: 0.1133
**********************************
Step: 141
Epoch: 1/1
D Loss: 2.8759
G Loss: 0.1456
**********************************
Step: 151
Epoch: 1/1
D Loss: 3.6215
G Loss: 0.0499
**********************************
Step: 161
Epoch: 1/1
D Loss: 3.2594
G Loss: 0.0774
**********************************
Step: 171
Epoch: 1/1
D Loss: 2.5635
G Loss: 0.1561
**********************************
Step: 181
Epoch: 1/1
D Loss: 2.8505
G Loss: 0.0921
**********************************
Step: 191
Epoch: 1/1
D Loss: 2.4449
G Loss: 0.1907
**********************************
Step: 201
Epoch: 1/1
D Loss: 3.1523
G Loss: 0.1484
**********************************
**********************************
Step: 211
Epoch: 1/1
D Loss: 3.5753
G Loss: 0.4279
**********************************
Step: 221
Epoch: 1/1
D Loss: 3.3859
G Loss: 0.0637
**********************************
Step: 231
Epoch: 1/1
D Loss: 2.7965
G Loss: 0.1085
**********************************
Step: 241
Epoch: 1/1
D Loss: 2.9575
G Loss: 0.1329
**********************************
Step: 251
Epoch: 1/1
D Loss: 3.6392
G Loss: 0.0547
**********************************
Step: 261
Epoch: 1/1
D Loss: 2.7648
G Loss: 0.2409
**********************************
Step: 271
Epoch: 1/1
D Loss: 2.5035
G Loss: 0.1502
**********************************
Step: 281
Epoch: 1/1
D Loss: 2.3360
G Loss: 0.4020
**********************************
Step: 291
Epoch: 1/1
D Loss: 3.2409
G Loss: 0.0843
**********************************
Step: 301
Epoch: 1/1
D Loss: 2.7666
G Loss: 0.1363
**********************************
**********************************
Step: 311
Epoch: 1/1
D Loss: 2.4273
G Loss: 0.1722
**********************************
Step: 321
Epoch: 1/1
D Loss: 1.7475
G Loss: 0.3326
**********************************
Step: 331
Epoch: 1/1
D Loss: 4.3581
G Loss: 0.0218
**********************************
Step: 341
Epoch: 1/1
D Loss: 4.7358
G Loss: 0.0172
**********************************
Step: 351
Epoch: 1/1
D Loss: 3.7384
G Loss: 0.0433
**********************************
Step: 361
Epoch: 1/1
D Loss: 3.9153
G Loss: 0.0394
**********************************
Step: 371
Epoch: 1/1
D Loss: 2.8190
G Loss: 0.1631
**********************************
Step: 381
Epoch: 1/1
D Loss: 3.3149
G Loss: 0.0660
**********************************
Step: 391
Epoch: 1/1
D Loss: 3.3234
G Loss: 0.0875
**********************************
Step: 401
Epoch: 1/1
D Loss: 3.4929
G Loss: 0.0634
**********************************
**********************************
Step: 411
Epoch: 1/1
D Loss: 3.1760
G Loss: 0.2980
**********************************
Step: 421
Epoch: 1/1
D Loss: 3.2720
G Loss: 0.0651
**********************************
Step: 431
Epoch: 1/1
D Loss: 1.3441
G Loss: 1.6836
**********************************
Step: 441
Epoch: 1/1
D Loss: 2.1837
G Loss: 0.2468
**********************************
Step: 451
Epoch: 1/1
D Loss: 3.0173
G Loss: 0.0807
**********************************
Step: 461
Epoch: 1/1
D Loss: 3.2403
G Loss: 0.0938
**********************************
Step: 471
Epoch: 1/1
D Loss: 2.5261
G Loss: 0.2842
**********************************
Step: 481
Epoch: 1/1
D Loss: 2.6252
G Loss: 0.1853
**********************************
Step: 491
Epoch: 1/1
D Loss: 2.2960
G Loss: 0.1900
**********************************
Step: 501
Epoch: 1/1
D Loss: 3.3347
G Loss: 1.3495
**********************************
**********************************
Step: 511
Epoch: 1/1
D Loss: 3.0159
G Loss: 0.1005
**********************************
Step: 521
Epoch: 1/1
D Loss: 1.9213
G Loss: 0.3168
**********************************
Step: 531
Epoch: 1/1
D Loss: 3.9475
G Loss: 0.0618
**********************************
Step: 541
Epoch: 1/1
D Loss: 3.6225
G Loss: 0.0575
**********************************
Step: 551
Epoch: 1/1
D Loss: 2.2118
G Loss: 0.2052
**********************************
Step: 561
Epoch: 1/1
D Loss: 1.0781
G Loss: 1.8791
**********************************
Step: 571
Epoch: 1/1
D Loss: 2.9229
G Loss: 0.0870
**********************************
Step: 581
Epoch: 1/1
D Loss: 2.5948
G Loss: 0.1288
**********************************
Step: 591
Epoch: 1/1
D Loss: 3.1792
G Loss: 0.2989
**********************************
Step: 601
Epoch: 1/1
D Loss: 1.7827
G Loss: 0.3687
**********************************
**********************************
Step: 611
Epoch: 1/1
D Loss: 2.7748
G Loss: 0.1121
**********************************
Step: 621
Epoch: 1/1
D Loss: 1.6836
G Loss: 0.4933
**********************************
Step: 631
Epoch: 1/1
D Loss: 1.7803
G Loss: 0.6995
**********************************
Step: 641
Epoch: 1/1
D Loss: 2.5350
G Loss: 0.1782
**********************************
Step: 651
Epoch: 1/1
D Loss: 2.2258
G Loss: 0.2098
**********************************
Step: 661
Epoch: 1/1
D Loss: 1.0083
G Loss: 1.3493
**********************************
Step: 671
Epoch: 1/1
D Loss: 2.2271
G Loss: 0.2213
**********************************
Step: 681
Epoch: 1/1
D Loss: 2.9383
G Loss: 0.0903
**********************************
Step: 691
Epoch: 1/1
D Loss: 2.9656
G Loss: 0.0917
**********************************
Step: 701
Epoch: 1/1
D Loss: 1.8496
G Loss: 1.8461
**********************************
**********************************
Step: 711
Epoch: 1/1
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G Loss: 0.5439
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G Loss: 0.2027
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G Loss: 0.5486
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G Loss: 0.0674
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G Loss: 0.1245
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G Loss: 0.0674
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G Loss: 0.0553
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G Loss: 0.1399
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G Loss: 0.1019
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G Loss: 0.1393
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G Loss: 0.0730
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G Loss: 0.0932
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G Loss: 0.0590
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Epoch: 1/1
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Epoch: 1/1
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G Loss: 0.1281
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Epoch: 1/1
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G Loss: 0.1524
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Epoch: 1/1
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G Loss: 0.5259
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Epoch: 1/1
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G Loss: 0.0866
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Epoch: 1/1
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G Loss: 0.0855
**********************************
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G Loss: 0.1012
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G Loss: 0.1130
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G Loss: 0.1012
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G Loss: 0.1910
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G Loss: 0.0961
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G Loss: 0.1193
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G Loss: 0.0995
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G Loss: 0.1054
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G Loss: 0.4488
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G Loss: 0.1512
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G Loss: 0.0354
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G Loss: 0.2301
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G Loss: 0.5416
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G Loss: 0.1128
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Epoch: 1/1
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G Loss: 0.0699
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G Loss: 0.0984
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G Loss: 0.1173
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Epoch: 1/1
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G Loss: 0.1421
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Epoch: 1/1
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G Loss: 0.4581
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G Loss: 0.1065
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Epoch: 1/1
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G Loss: 0.0783
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Epoch: 1/1
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G Loss: 0.0468
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G Loss: 0.1165
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Epoch: 1/1
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G Loss: 0.0525
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G Loss: 0.0554
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G Loss: 0.0522
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G Loss: 0.0991
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Epoch: 1/1
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G Loss: 0.0778
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G Loss: 0.1101
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Epoch: 1/1
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G Loss: 0.0700
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G Loss: 0.0737
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Epoch: 1/1
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G Loss: 0.2211
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G Loss: 0.0626
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G Loss: 0.0358
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G Loss: 0.1606
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G Loss: 0.0752
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G Loss: 0.0788
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G Loss: 0.1462
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G Loss: 0.1194
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G Loss: 0.1140
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G Loss: 0.0883
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G Loss: 0.1003
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G Loss: 0.2723
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G Loss: 0.0398
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G Loss: 0.0587
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G Loss: 0.0781
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G Loss: 0.0967
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G Loss: 0.1447
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G Loss: 0.0485
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G Loss: 0.0767
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G Loss: 0.0964
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G Loss: 0.0726
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G Loss: 0.0672
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G Loss: 0.0402
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G Loss: 0.2738
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G Loss: 0.0617
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G Loss: 0.1243
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G Loss: 0.2405
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G Loss: 0.0959
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D Loss: 3.1547
G Loss: 0.5254
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D Loss: 2.8920
G Loss: 0.0989
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G Loss: 0.0860
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G Loss: 0.1307
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G Loss: 0.0636
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G Loss: 0.0490
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G Loss: 0.0755
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G Loss: 0.1416
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G Loss: 0.0815
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G Loss: 0.0449
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G Loss: 0.0688
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G Loss: 0.0743
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G Loss: 0.0791
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G Loss: 0.0554
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G Loss: 0.1114
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G Loss: 0.0554
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G Loss: 0.1254
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G Loss: 0.0994
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G Loss: 0.0669
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G Loss: 0.1885
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Epoch: 1/1
D Loss: 3.3370
G Loss: 0.0801
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D Loss: 3.3944
G Loss: 0.0524
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G Loss: 0.0662
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G Loss: 0.0894
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G Loss: 0.0382
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G Loss: 0.0657
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G Loss: 0.0727
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G Loss: 0.0698
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G Loss: 0.0590
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G Loss: 0.0586
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G Loss: 0.0623
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G Loss: 0.0385
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G Loss: 0.0763
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G Loss: 0.2491
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G Loss: 0.0767
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G Loss: 0.0652
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G Loss: 0.0683
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G Loss: 0.0985
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G Loss: 0.0674
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G Loss: 0.0629
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G Loss: 0.0657
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G Loss: 0.0309
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G Loss: 0.1139
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G Loss: 0.1115
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G Loss: 0.0824
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G Loss: 0.0522
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G Loss: 0.0582
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G Loss: 0.1471
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G Loss: 0.0681
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G Loss: 0.0633
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G Loss: 0.0814
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G Loss: 0.3169
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G Loss: 0.0496
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G Loss: 0.0547
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G Loss: 0.1117
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G Loss: 0.0737
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G Loss: 0.0997
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G Loss: 0.0465
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G Loss: 0.0754
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G Loss: 0.1587
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D Loss: 3.8726
G Loss: 0.0352
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D Loss: 3.2251
G Loss: 0.0798
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G Loss: 0.1192
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D Loss: 3.3139
G Loss: 0.0602
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D Loss: 3.1067
G Loss: 0.0757
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G Loss: 0.0378
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G Loss: 0.1154
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D Loss: 3.7161
G Loss: 0.0410
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D Loss: 3.0622
G Loss: 0.0781
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D Loss: 3.3845
G Loss: 0.0617
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D Loss: 4.3999
G Loss: 0.0302
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D Loss: 3.1781
G Loss: 0.0748
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D Loss: 3.4356
G Loss: 0.0568
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D Loss: 3.3935
G Loss: 0.0653
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D Loss: 3.0296
G Loss: 0.0848
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D Loss: 3.4467
G Loss: 0.0568
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D Loss: 3.4478
G Loss: 0.1285
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D Loss: 3.2417
G Loss: 0.0642
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D Loss: 3.8590
G Loss: 0.0357
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D Loss: 3.4157
G Loss: 0.0604
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D Loss: 3.4712
G Loss: 0.0532
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D Loss: 3.8647
G Loss: 0.0356
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D Loss: 3.0753
G Loss: 0.0775
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G Loss: 0.0180
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D Loss: 3.7560
G Loss: 0.0379
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G Loss: 0.0560
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D Loss: 4.2102
G Loss: 0.0405
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D Loss: 2.3644
G Loss: 0.2062
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D Loss: 3.2115
G Loss: 0.0749
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D Loss: 3.6028
G Loss: 0.0447
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D Loss: 3.2425
G Loss: 0.0787
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D Loss: 3.7232
G Loss: 0.0433
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D Loss: 3.5415
G Loss: 0.0553
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D Loss: 3.8538
G Loss: 0.0364
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D Loss: 2.9089
G Loss: 0.1001
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D Loss: 2.9779
G Loss: 0.0987
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D Loss: 3.1962
G Loss: 0.0732
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D Loss: 3.5035
G Loss: 0.0509
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D Loss: 2.8633
G Loss: 0.1536
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D Loss: 3.8166
G Loss: 0.0383
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D Loss: 3.2198
G Loss: 0.0759
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D Loss: 3.1654
G Loss: 0.0758
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D Loss: 3.8902
G Loss: 0.0346
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D Loss: 4.0325
G Loss: 0.0317
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D Loss: 2.7066
G Loss: 0.1209
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D Loss: 2.3151
G Loss: 0.3429
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D Loss: 3.3603
G Loss: 0.0788
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D Loss: 3.3099
G Loss: 0.0645
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D Loss: 3.9435
G Loss: 0.0324
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D Loss: 3.5540
G Loss: 0.0532
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D Loss: 3.0475
G Loss: 0.0800
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D Loss: 3.5461
G Loss: 0.0578
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D Loss: 3.8447
G Loss: 0.0360
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D Loss: 3.4979
G Loss: 0.0514
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D Loss: 2.6992
G Loss: 0.1703
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D Loss: 3.8718
G Loss: 0.0364
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G Loss: 0.0737
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G Loss: 0.1067
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G Loss: 0.0266
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G Loss: 0.0420
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G Loss: 0.0981
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G Loss: 0.0477
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G Loss: 0.2090
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G Loss: 0.0912
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G Loss: 0.0644
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G Loss: 0.1949
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G Loss: 0.1121
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G Loss: 0.0201
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G Loss: 0.0502
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G Loss: 0.0430
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G Loss: 0.0767
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G Loss: 0.0306
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G Loss: 0.0338
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G Loss: 0.0730
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G Loss: 0.0668
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G Loss: 0.0590
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G Loss: 0.1051
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G Loss: 0.0458
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G Loss: 0.0530
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G Loss: 0.0843
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G Loss: 0.1650
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G Loss: 0.0390
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G Loss: 0.1105
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G Loss: 0.0612
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G Loss: 0.0955
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G Loss: 0.1885
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G Loss: 0.0363
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G Loss: 0.0662
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G Loss: 0.0504
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G Loss: 0.0353
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G Loss: 0.1299
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G Loss: 0.1825
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G Loss: 0.0600
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G Loss: 0.1569
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G Loss: 0.0547
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G Loss: 0.0816
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G Loss: 0.0554
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G Loss: 0.0423
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Epoch: 1/1
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G Loss: 0.0298
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G Loss: 0.0666
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G Loss: 0.0416
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G Loss: 0.0552
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Epoch: 1/1
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G Loss: 0.1805
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Epoch: 1/1
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G Loss: 0.0423
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Epoch: 1/1
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G Loss: 0.0552
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Epoch: 1/1
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G Loss: 0.0912
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Epoch: 1/1
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G Loss: 0.0243
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Epoch: 1/1
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G Loss: 0.0640
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Epoch: 1/1
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G Loss: 0.0976
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Epoch: 1/1
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G Loss: 0.0301
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Epoch: 1/1
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G Loss: 0.0668
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Epoch: 1/1
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G Loss: 0.0685
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Epoch: 1/1
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G Loss: 0.0584
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G Loss: 0.0422
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G Loss: 0.1286
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Epoch: 1/1
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G Loss: 0.0796
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Epoch: 1/1
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G Loss: 0.0385
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Epoch: 1/1
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G Loss: 0.0631
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Epoch: 1/1
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G Loss: 0.0366
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G Loss: 0.0408
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G Loss: 0.0765
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G Loss: 0.1256
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G Loss: 0.0386
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Epoch: 1/1
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G Loss: 0.0721
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G Loss: 0.0425
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G Loss: 0.0512
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G Loss: 0.0631
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G Loss: 0.0710
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G Loss: 0.0828
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G Loss: 0.0656
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G Loss: 0.0502
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G Loss: 0.0483
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G Loss: 0.0619
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G Loss: 0.0549
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G Loss: 0.0482
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G Loss: 0.0312
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G Loss: 0.0935
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G Loss: 0.0643
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G Loss: 0.0680
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G Loss: 0.0458
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G Loss: 0.0383
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G Loss: 0.0532
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G Loss: 0.0422
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G Loss: 0.3736
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G Loss: 0.0720
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G Loss: 0.0580
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G Loss: 0.0759
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G Loss: 0.0660
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G Loss: 0.0847
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G Loss: 0.0363
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G Loss: 0.2525
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G Loss: 0.0583
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G Loss: 0.0659
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G Loss: 0.0194
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G Loss: 0.0394
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G Loss: 0.0513
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G Loss: 0.1042
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G Loss: 0.0533
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G Loss: 0.0947
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G Loss: 0.1086
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G Loss: 0.0869
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G Loss: 0.0656
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G Loss: 0.0119
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G Loss: 0.0792
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G Loss: 0.0558
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G Loss: 0.0545
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G Loss: 0.1239
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G Loss: 0.0671
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G Loss: 0.0831
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G Loss: 0.1182
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G Loss: 0.0793
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G Loss: 0.1048
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G Loss: 0.0617
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G Loss: 0.0472
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G Loss: 0.1028
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G Loss: 0.0696
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G Loss: 0.0597
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G Loss: 0.0285
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G Loss: 0.0599
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G Loss: 0.0339
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G Loss: 0.0472
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G Loss: 0.0407
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G Loss: 0.0371
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G Loss: 0.0376
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D Loss: 3.5124
G Loss: 0.0559
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G Loss: 0.0786
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G Loss: 0.0453
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G Loss: 0.0381
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G Loss: 0.0170
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G Loss: 0.0269
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G Loss: 0.1598
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G Loss: 0.0386
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G Loss: 0.0299
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D Loss: 3.4905
G Loss: 0.0478
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D Loss: 4.0999
G Loss: 0.0331
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G Loss: 0.0620
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G Loss: 0.0837
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G Loss: 0.0302
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G Loss: 0.0582
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G Loss: 0.0717
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G Loss: 0.0689
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G Loss: 0.1203
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G Loss: 0.0343
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D Loss: 3.5970
G Loss: 0.0526
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G Loss: 0.0564
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G Loss: 0.0380
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G Loss: 0.0642
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D Loss: 3.8042
G Loss: 0.0353
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G Loss: 0.0401
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G Loss: 0.0900
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G Loss: 0.0729
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G Loss: 0.0745
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D Loss: 3.2246
G Loss: 0.0626
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D Loss: 3.2527
G Loss: 0.0713
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D Loss: 3.6877
G Loss: 0.0502
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G Loss: 0.0333
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G Loss: 0.0463
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G Loss: 0.0321
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D Loss: 3.4041
G Loss: 0.0629
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G Loss: 0.0406
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G Loss: 0.0843
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D Loss: 3.6550
G Loss: 0.0453
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D Loss: 3.0247
G Loss: 0.0865
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D Loss: 2.1246
G Loss: 0.2091
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D Loss: 2.5396
G Loss: 0.3698
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D Loss: 3.0503
G Loss: 0.0883
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D Loss: 3.4831
G Loss: 0.0603
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D Loss: 3.3794
G Loss: 0.0593
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D Loss: 3.2338
G Loss: 0.0741
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D Loss: 4.1941
G Loss: 0.0281
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D Loss: 3.1805
G Loss: 0.0778
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D Loss: 3.2951
G Loss: 0.0737
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D Loss: 3.2936
G Loss: 0.0590
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D Loss: 2.6249
G Loss: 1.1170
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D Loss: 3.6908
G Loss: 0.0471
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D Loss: 3.7374
G Loss: 0.0411
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D Loss: 3.7086
G Loss: 0.0457
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D Loss: 2.7569
G Loss: 0.1022
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D Loss: 3.0806
G Loss: 0.0793
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D Loss: 1.7499
G Loss: 0.3573
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D Loss: 4.8240
G Loss: 0.0135
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D Loss: 3.2820
G Loss: 0.0702
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D Loss: 2.9861
G Loss: 0.0825
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D Loss: 1.9264
G Loss: 0.2835
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D Loss: 3.3322
G Loss: 0.0794
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D Loss: 2.9444
G Loss: 0.1017
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D Loss: 3.2056
G Loss: 0.0676
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D Loss: 3.5553
G Loss: 0.0463
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D Loss: 3.7754
G Loss: 0.0424
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D Loss: 3.2181
G Loss: 0.0703
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D Loss: 3.6897
G Loss: 0.0397
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D Loss: 3.3200
G Loss: 0.0670
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D Loss: 3.3050
G Loss: 0.0644
**********************************
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Epoch: 1/1
D Loss: 3.4151
G Loss: 0.0596
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Epoch: 1/1
D Loss: 2.6305
G Loss: 0.1445
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Epoch: 1/1
D Loss: 3.0330
G Loss: 0.1055
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Epoch: 1/1
D Loss: 2.2956
G Loss: 0.2102
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Epoch: 1/1
D Loss: 3.5745
G Loss: 0.0549
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Epoch: 1/1
D Loss: 2.2663
G Loss: 0.1846
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Epoch: 1/1
D Loss: 3.1866
G Loss: 0.0898
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Epoch: 1/1
D Loss: 3.9847
G Loss: 0.0318
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Epoch: 1/1
D Loss: 3.3612
G Loss: 0.0639
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Epoch: 1/1
D Loss: 3.7313
G Loss: 0.0425
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Epoch: 1/1
D Loss: 3.1804
G Loss: 0.0753
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Epoch: 1/1
D Loss: 4.5570
G Loss: 0.0175
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Epoch: 1/1
D Loss: 3.3397
G Loss: 0.0708
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Epoch: 1/1
D Loss: 4.3027
G Loss: 0.0243
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Epoch: 1/1
D Loss: 2.9943
G Loss: 0.1712
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Epoch: 1/1
D Loss: 3.6734
G Loss: 0.0429
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Epoch: 1/1
D Loss: 3.4018
G Loss: 0.0668
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Epoch: 1/1
D Loss: 3.8385
G Loss: 0.0414
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Epoch: 1/1
D Loss: 3.1634
G Loss: 0.0802
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Epoch: 1/1
D Loss: 3.3015
G Loss: 0.0638
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Epoch: 1/1
D Loss: 3.7788
G Loss: 0.0503
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Epoch: 1/1
D Loss: 3.7991
G Loss: 0.0461
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Epoch: 1/1
D Loss: 3.3994
G Loss: 0.0590
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Epoch: 1/1
D Loss: 3.0501
G Loss: 0.0793
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Epoch: 1/1
D Loss: 3.8767
G Loss: 0.0408
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Epoch: 1/1
D Loss: 3.8530
G Loss: 0.0526
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Epoch: 1/1
D Loss: 3.8970
G Loss: 0.0341
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Epoch: 1/1
D Loss: 3.6206
G Loss: 0.0466
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Epoch: 1/1
D Loss: 3.4147
G Loss: 0.0538
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Epoch: 1/1
D Loss: 3.3727
G Loss: 0.0949
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Epoch: 1/1
D Loss: 2.6630
G Loss: 0.1372
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Epoch: 1/1
D Loss: 2.5398
G Loss: 0.1752
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Epoch: 1/1
D Loss: 3.9343
G Loss: 0.0375
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Epoch: 1/1
D Loss: 3.4242
G Loss: 0.0596
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Epoch: 1/1
D Loss: 3.0049
G Loss: 0.0874
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Epoch: 1/1
D Loss: 3.2958
G Loss: 0.0687
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Epoch: 1/1
D Loss: 3.9646
G Loss: 0.0305
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Epoch: 1/1
D Loss: 3.4895
G Loss: 0.0676
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Epoch: 1/1
D Loss: 3.1586
G Loss: 0.0732
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Epoch: 1/1
D Loss: 2.1712
G Loss: 0.2441
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Epoch: 1/1
D Loss: 3.1616
G Loss: 0.0747
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Epoch: 1/1
D Loss: 3.7239
G Loss: 0.0734
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Epoch: 1/1
D Loss: 1.6758
G Loss: 0.4842
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Epoch: 1/1
D Loss: 3.4048
G Loss: 0.0622
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Epoch: 1/1
D Loss: 4.4656
G Loss: 0.0210
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Epoch: 1/1
D Loss: 4.0152
G Loss: 0.0334
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Epoch: 1/1
D Loss: 2.8811
G Loss: 0.1222
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Epoch: 1/1
D Loss: 3.4572
G Loss: 0.0557
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Epoch: 1/1
D Loss: 3.3281
G Loss: 0.0619
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Epoch: 1/1
D Loss: 3.6567
G Loss: 0.0433
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Epoch: 1/1
D Loss: 3.4306
G Loss: 0.0528
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Epoch: 1/1
D Loss: 3.4725
G Loss: 0.0629
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Epoch: 1/1
D Loss: 3.9007
G Loss: 0.0405
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Epoch: 1/1
D Loss: 3.0707
G Loss: 0.0782
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Epoch: 1/1
D Loss: 3.4255
G Loss: 0.0553
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Epoch: 1/1
D Loss: 2.2075
G Loss: 0.2067
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Epoch: 1/1
D Loss: 3.7759
G Loss: 0.0484
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Epoch: 1/1
D Loss: 3.3282
G Loss: 0.0686
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Epoch: 1/1
D Loss: 3.5560
G Loss: 0.0534
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Epoch: 1/1
D Loss: 3.5412
G Loss: 0.0536
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Epoch: 1/1
D Loss: 3.4775
G Loss: 0.0507
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Epoch: 1/1
D Loss: 4.1298
G Loss: 0.0326
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Epoch: 1/1
D Loss: 2.9350
G Loss: 0.1676
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Epoch: 1/1
D Loss: 3.4905
G Loss: 0.0678
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Epoch: 1/1
D Loss: 3.1961
G Loss: 0.0654
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Epoch: 1/1
D Loss: 4.0208
G Loss: 0.0339
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Epoch: 1/1
D Loss: 3.6694
G Loss: 0.0457
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Epoch: 1/1
D Loss: 4.7017
G Loss: 0.0183
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Epoch: 1/1
D Loss: 3.4424
G Loss: 0.0610
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Epoch: 1/1
D Loss: 3.2799
G Loss: 0.0687
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Epoch: 1/1
D Loss: 3.4192
G Loss: 0.0601
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Epoch: 1/1
D Loss: 3.2670
G Loss: 0.1059
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Epoch: 1/1
D Loss: 3.5800
G Loss: 0.0492
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Epoch: 1/1
D Loss: 3.5461
G Loss: 0.0550
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Epoch: 1/1
D Loss: 3.1805
G Loss: 0.0808
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Epoch: 1/1
D Loss: 2.9814
G Loss: 0.0947
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Epoch: 1/1
D Loss: 2.2030
G Loss: 0.2417
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Epoch: 1/1
D Loss: 3.6233
G Loss: 0.0468
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Epoch: 1/1
D Loss: 3.4767
G Loss: 0.0533
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Epoch: 1/1
D Loss: 2.7477
G Loss: 0.1110
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Epoch: 1/1
D Loss: 2.8531
G Loss: 0.1164
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Epoch: 1/1
D Loss: 3.4922
G Loss: 0.0481
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Epoch: 1/1
D Loss: 3.4991
G Loss: 0.0495
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Epoch: 1/1
D Loss: 3.6338
G Loss: 0.0551
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Epoch: 1/1
D Loss: 3.4948
G Loss: 0.0615
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Epoch: 1/1
D Loss: 3.7554
G Loss: 0.0451
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Epoch: 1/1
D Loss: 3.2427
G Loss: 0.0655
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Epoch: 1/1
D Loss: 3.9809
G Loss: 0.0352
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Epoch: 1/1
D Loss: 3.4939
G Loss: 0.0522
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Epoch: 1/1
D Loss: 1.8992
G Loss: 0.3892
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Epoch: 1/1
D Loss: 3.5745
G Loss: 0.0559
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Epoch: 1/1
D Loss: 4.4271
G Loss: 0.0218
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Epoch: 1/1
D Loss: 4.0642
G Loss: 0.0347
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Epoch: 1/1
D Loss: 3.4138
G Loss: 0.0640
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Epoch: 1/1
D Loss: 3.0363
G Loss: 0.1095
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Epoch: 1/1
D Loss: 4.3764
G Loss: 0.0227
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Epoch: 1/1
D Loss: 3.0167
G Loss: 0.0793
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Epoch: 1/1
D Loss: 3.4804
G Loss: 0.0533
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Epoch: 1/1
D Loss: 3.2693
G Loss: 0.1009
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Epoch: 1/1
D Loss: 3.6551
G Loss: 0.1594
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Epoch: 1/1
D Loss: 3.3501
G Loss: 0.0688
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Epoch: 1/1
D Loss: 3.7204
G Loss: 0.0443
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Epoch: 1/1
D Loss: 3.6434
G Loss: 0.0487
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Epoch: 1/1
D Loss: 3.5620
G Loss: 0.0756
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Epoch: 1/1
D Loss: 1.5361
G Loss: 0.4938
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Step: 6261
Epoch: 1/1
D Loss: 3.4409
G Loss: 0.0582
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Step: 6271
Epoch: 1/1
D Loss: 2.4633
G Loss: 0.2257
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Step: 6281
Epoch: 1/1
D Loss: 4.0270
G Loss: 0.0329
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Step: 6291
Epoch: 1/1
D Loss: 4.0687
G Loss: 0.0342
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Step: 6301
Epoch: 1/1
D Loss: 2.2463
G Loss: 0.2522
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**********************************
Step: 6311
Epoch: 1/1
D Loss: 3.2566
G Loss: 0.0749
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Step: 6321
Epoch: 1/1
D Loss: 1.6290
G Loss: 0.6043
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Step: 6331
Epoch: 1/1
D Loss: 3.5617
G Loss: 0.0533

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.